module_clust: module_clust

In miRSM: Inferring miRNA sponge modules in heterogeneous data

Description

Identification of gene modules from matched ceRNA and mRNA expression data using a series of clustering packages, including stats, flashClust, dbscan, subspace, mclust, SOMbrero and ppclust packages.

Usage

module_clust(
  ceRExp,
  mRExp,
  cluster.method = "kmeans",
  num.modules = 10,
  num.ModuleceRs = 2,
  num.ModulemRs = 2
)

Arguments

ceRExp	A SummarizedExperiment object. ceRNA expression data: rows are samples and columns are ceRNAs.
mRExp	A SummarizedExperiment object. mRNA expression data: rows are samples and columns are mRNAs.
cluster.method	Specification of the clustering method, including 'kmeans'(default), 'hclust', 'dbscan' , 'clique', 'gmm', 'som' and 'fcm'.
num.modules	Parameter of the number of modules to be identified for the 'kmeans', 'hclust', 'gmm' and 'fcm' methods. Parameter of the number of intervals for the 'clique' method. For the 'dbscan' and 'som' methods, no need to set the parameter.
num.ModuleceRs	The minimum number of ceRNAs in each module.
num.ModulemRs	The minimum number of mRNAs in each module.

Value

GeneSetCollection object: a list of module genes.

Author(s)

Junpeng Zhang (https://www.researchgate.net/profile/Junpeng_Zhang3)

References

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MacQueen J. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1967, 1, pp.281-297. Berkeley, CA: University of California Press.

Langfelder P, Horvath S. Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software. 2012, 46(11):1-17.

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Campello RJGB, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates, Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, 2013: 160-172.

Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. ACM SIGMOD, 1998.

Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models The R Journal 8/1, 2016, pp. 205-233.

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Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 1973, 3(3):32-57.

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Bezdek JC. Pattern recognition with fuzzy objective function algorithms. Plenum, NY, 1981.

Examples

data(BRCASampleData)
modulegenes_clust <- module_clust(ceRExp[, seq_len(30)],
    mRExp[, seq_len(30)])

miRSM documentation built on April 16, 2021, 6 p.m.